--- comments: true description: Discover how to detect objects with rotation for higher precision using YOLO11 OBB models. Learn, train, validate, and export OBB models effortlessly. keywords: Oriented Bounding Boxes, OBB, Object Detection, YOLO11, Ultralytics, DOTAv1, Model Training, Model Export, AI, Machine Learning model_name: yolo11n-obb --- # Oriented Bounding Boxes [Object Detection](https://www.ultralytics.com/glossary/object-detection) Oriented object detection goes a step further than standard object detection by introducing an extra angle to locate objects more accurately in an image. The output of an oriented object detector is a set of rotated bounding boxes that precisely enclose the objects in the image, along with class labels and confidence scores for each box. Oriented bounding boxes are particularly useful when objects appear at various angles, such as in aerial imagery, where traditional axis-aligned bounding boxes may include unnecessary background. !!! tip YOLO11 OBB models use the `-obb` suffix, i.e. `yolo11n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).



Watch: Object Detection using Ultralytics YOLO Oriented Bounding Boxes (YOLO-OBB)

## Visual Samples | Ships Detection using OBB | Vehicle Detection using OBB | | :------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------: | | ![Ships Detection using OBB](https://github.com/ultralytics/docs/releases/download/0/ships-detection-using-obb.avif) | ![Vehicle Detection using OBB](https://github.com/ultralytics/docs/releases/download/0/vehicle-detection-using-obb.avif) | ## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11) YOLO11 pretrained OBB models are shown here, which are pretrained on the [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) dataset. [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use. {% include "macros/yolo-obb-perf.md" %} - **mAPtest** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset.
Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html). - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu` ## Train Train YOLO11n-obb on the DOTA8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page. !!! example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n-obb.yaml") # build a new model from YAML model = YOLO("yolo11n-obb.pt") # load a pretrained model (recommended for training) model = YOLO("yolo11n-obb.yaml").load("yolo11n.pt") # build from YAML and transfer weights # Train the model results = model.train(data="dota8.yaml", epochs=100, imgsz=640) ``` === "CLI" ```bash # Build a new model from YAML and start training from scratch yolo obb train data=dota8.yaml model=yolo11n-obb.yaml epochs=100 imgsz=640 # Start training from a pretrained *.pt model yolo obb train data=dota8.yaml model=yolo11n-obb.pt epochs=100 imgsz=640 # Build a new model from YAML, transfer pretrained weights to it and start training yolo obb train data=dota8.yaml model=yolo11n-obb.yaml pretrained=yolo11n-obb.pt epochs=100 imgsz=640 ```



Watch: How to Train Ultralytics YOLO-OBB (Oriented Bounding Boxes) Models on DOTA Dataset using Ultralytics HUB

### Dataset format OBB dataset format can be found in detail in the [Dataset Guide](../datasets/obb/index.md). The YOLO OBB format designates bounding boxes by their four corner points with coordinates normalized between 0 and 1, following this structure: ``` class_index x1 y1 x2 y2 x3 y3 x4 y4 ``` Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the [bounding box](https://www.ultralytics.com/glossary/bounding-box)'s center point (xy), width, height, and rotation. ## Val Validate trained YOLO11n-obb model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the DOTA8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes. !!! example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n-obb.pt") # load an official model model = YOLO("path/to/best.pt") # load a custom model # Validate the model metrics = model.val(data="dota8.yaml") # no arguments needed, dataset and settings remembered metrics.box.map # map50-95(B) metrics.box.map50 # map50(B) metrics.box.map75 # map75(B) metrics.box.maps # a list contains map50-95(B) of each category ``` === "CLI" ```bash yolo obb val model=yolo11n-obb.pt data=dota8.yaml # val official model yolo obb val model=path/to/best.pt data=path/to/data.yaml # val custom model ``` ## Predict Use a trained YOLO11n-obb model to run predictions on images. !!! example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n-obb.pt") # load an official model model = YOLO("path/to/best.pt") # load a custom model # Predict with the model results = model("https://ultralytics.com/images/boats.jpg") # predict on an image # Access the results for result in results: xywhr = result.obb.xywhr # center-x, center-y, width, height, angle (radians) xyxyxyxy = result.obb.xyxyxyxy # polygon format with 4-points names = [result.names[cls.item()] for cls in result.obb.cls.int()] # class name of each box confs = result.obb.conf # confidence score of each box ``` === "CLI" ```bash yolo obb predict model=yolo11n-obb.pt source='https://ultralytics.com/images/boats.jpg' # predict with official model yolo obb predict model=path/to/best.pt source='https://ultralytics.com/images/boats.jpg' # predict with custom model ```



Watch: How to Detect and Track Storage Tanks using Ultralytics YOLO-OBB | Oriented Bounding Boxes | DOTA

See full `predict` mode details in the [Predict](../modes/predict.md) page. ## Export Export a YOLO11n-obb model to a different format like ONNX, CoreML, etc. !!! example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n-obb.pt") # load an official model model = YOLO("path/to/best.pt") # load a custom trained model # Export the model model.export(format="onnx") ``` === "CLI" ```bash yolo export model=yolo11n-obb.pt format=onnx # export official model yolo export model=path/to/best.pt format=onnx # export custom trained model ``` Available YOLO11-obb export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolo11n-obb.onnx`. Usage examples are shown for your model after export completes. {% include "macros/export-table.md" %} See full `export` details in the [Export](../modes/export.md) page. ## Real-World Applications OBB detection with YOLO11 has numerous practical applications across various industries: - **Maritime and Port Management**: Detecting ships and vessels at various angles for [fleet management](https://www.ultralytics.com/blog/how-to-use-ultralytics-yolo11-for-obb-object-detection) and monitoring. - **Urban Planning**: Analyzing buildings and infrastructure from aerial imagery. - **Agriculture**: Monitoring crops and agricultural equipment from drone footage. - **Energy Sector**: Inspecting solar panels and wind turbines at different orientations. - **Transportation**: Tracking vehicles on roads and in parking lots from various perspectives. These applications benefit from OBB's ability to precisely fit objects at any angle, providing more accurate detection than traditional bounding boxes. ## FAQ ### What are Oriented Bounding Boxes (OBB) and how do they differ from regular bounding boxes? Oriented Bounding Boxes (OBB) include an additional angle to enhance object localization accuracy in images. Unlike regular bounding boxes, which are axis-aligned rectangles, OBBs can rotate to fit the orientation of the object better. This is particularly useful for applications requiring precise object placement, such as aerial or satellite imagery ([Dataset Guide](../datasets/obb/index.md)). ### How do I train a YOLO11n-obb model using a custom dataset? To train a YOLO11n-obb model with a custom dataset, follow the example below using Python or CLI: !!! example === "Python" ```python from ultralytics import YOLO # Load a pretrained model model = YOLO("yolo11n-obb.pt") # Train the model results = model.train(data="path/to/custom_dataset.yaml", epochs=100, imgsz=640) ``` === "CLI" ```bash yolo obb train data=path/to/custom_dataset.yaml model=yolo11n-obb.pt epochs=100 imgsz=640 ``` For more training arguments, check the [Configuration](../usage/cfg.md) section. ### What datasets can I use for training YOLO11-OBB models? YOLO11-OBB models are pretrained on datasets like [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml) but you can use any dataset formatted for OBB. Detailed information on OBB dataset formats can be found in the [Dataset Guide](../datasets/obb/index.md). ### How can I export a YOLO11-OBB model to ONNX format? Exporting a YOLO11-OBB model to ONNX format is straightforward using either Python or CLI: !!! example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n-obb.pt") # Export the model model.export(format="onnx") ``` === "CLI" ```bash yolo export model=yolo11n-obb.pt format=onnx ``` For more export formats and details, refer to the [Export](../modes/export.md) page. ### How do I validate the accuracy of a YOLO11n-obb model? To validate a YOLO11n-obb model, you can use Python or CLI commands as shown below: !!! example === "Python" ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n-obb.pt") # Validate the model metrics = model.val(data="dota8.yaml") ``` === "CLI" ```bash yolo obb val model=yolo11n-obb.pt data=dota8.yaml ``` See full validation details in the [Val](../modes/val.md) section.